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All Journal AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan JURNAL ILMIAH MANAJEMEN BISNIS DAN TERAPAN Fokus Bisnis Kubik Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MANAJEMEN MOTIVASI Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Agregat: Jurnal Ekonomi dan Bisnis BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Ekonomi dan Bisnis Jurnal Ilmiah Akuntansi dan Keuangan KUBIK: Jurnal Publikasi Ilmiah Matematika Jurnal Bisnis, Manajemen, dan Akuntansi Journal of International Conference Proceedings Euclid Journal of Data Science Theory and Application JCSE: Journal of Community Service and Empowerment
Windasari, Wahyuni
Universitas Putra Bangsa
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Understanding Work Values and Career Preference in Generation Z: is Becoming a Civil Servant Still a Dream Job? Ma'rifah, Diana; Azizah, Siti Nur; Windasari, Wahyuni
Journal of International Conference Proceedings Vol 7, No 1 (2024): 2024 ICPM Malaysia Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v7i1.2990

Abstract

Generation Z is currently entering the working world. However, there are reports that generation Z is uninterested in working for the government agency, thus government agencies need to understand the principles and values held by this young generation,  so as not to lose potential human resources in the future. Through an analysis of the work values dimensions, this study aims to identify the career preference of generation Z. The respondens in this study were 102 generation Z students at Putra Bangsa University, Indonesia. Data was obtained by distributing questionnaires and then analyzed with descriptive statistics via SPSS. The results show that the majority of generation Z students prefer to work at State-Owned Enterprises or Badan Usaha Milik Negara (BUMN) rather than being civil servants when they graduate from university. The work values believed by the group who want to work in BUMN and the group who want to become civil servants are explained in the results of this study. The results of this study provide an overview of the work values that are the principles of generation Z, so that it can help organizations understand the characteristics of generation Z in the workplace.
Pelatihan Perhitungan Harga Pokok Produksi Berbasis Microsoft Excel Windasari, Wahyuni; Zakiyah, Tuti; Kabul Trifiyanto
JCSE: Journal of Community Service and Empowerment Vol. 3 No. 2 (2022): JCSE Oktober 2022
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/jcse.v3i2.198

Abstract

This community service activity was motivated by the Kebumen Regency Youth Care and Pioneering Development concern for MSME. One of the basic problems that are often faced by MSME is determining the cost of production. Currently, the selling price set by MSME is an estimated selling price or even follows competitors' prices. The lack of knowledge related to accounting and financial management is one of the factors driving MSME not to apply HPP calculations in determining the selling price of their products. This can cause MSME errors in compiling income statements. This community service activity aims to increase the knowledge of MSME regarding the determination of HPP based on financial management and accounting knowledge while at the same time increasing their soft skills in calculations using Microsoft Excel. The results of this service activity, MSME can classify fixed cost components and variable costs, determine production costs and overhead costs, and calculate HPP either manually or using Ms. Excel based accounting calculations.  
Prediction of Financial Distress in Retail Companies Using Long-Short Term Memory (LSTM) Windasari, Wahyuni; Zakiyah, Tuti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6217

Abstract

Financial distress is a condition in which an entity struggles to meet its debt and operating obligations.. Financial distress can lead to bankruptcy or company closure if corrective action is not taken. This study aims to forecast financial distress in retail companies by utilizing key financial ratios, including Total Asset Turnover (TATO), Current Ratio (CR), Return on Assets (ROA), and Debt-to-Equity Ratio (DER). The analysis is based on secondary data from Indonesian retail companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. The dataset exhibited missing values and class imbalance, which were addressed using mean imputation and the Synthetic Minority Oversampling Technique (SMOTE), respectivelyTo perform predictions, a Long Short-Term Memory (LSTM) model was implemented. The integration of SMOTE contributed to enhanced detection of the minority class; however, it was accompanied by a slight reduction in overall predictive accuracy. The model demonstrated a performance accuracy of 86%, with a recall rate of 85%, a precision of 100%, and an F1-score of 92%.
HYBRID ARIMA–ANN MODEL FOR AIR QUALITY INDEX PREDICTION IN DKI JAKARTA Windasari, Wahyuni; Pradani, Augistri Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2335-2346

Abstract

Air pollution is a threat to all countries, including Indonesia. One area in Indonesia with poor air quality is DKI Jakarta. One step to minimize the decline in air quality in an area is to predict the air quality index in the future. In this study, a hybrid ARIMA-ANN analysis was conducted, combining the ARIMA method and Artificial Neural Networks to model air quality in DKI Jakarta. The time series data of the air quality index sourced from the DKI Jakarta Environmental Service during January 19-30, 2023, which was observed every hour with a total of 288 data. The results of the study showed that the SAE and RMSE of the ARIMA model were 94.135 and 1.157, respectively, while the SAE and RMSE values ​​of the hybrid ARIMA-ANN model were 61.094 and 1.15. The results of the study showed that the hybrid ARIMA-ANN model had a higher accuracy value compared to the single ARIMA model in describing DKI Jakarta air quality data. This study has limitations in that determining the network architecture in the ANN model is still done by trial and error, so it takes a relatively longer time.
Modelling of Forecasting ASEAN-5 Stock Price Index Using GSTAR Model Zakiyah, Tuti; Windasari, Wahyuni
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22738

Abstract

This research aims to apply the Generalized Space-Time Autoregressive (GSTAR) model to predict stock price indices in ASEAN-5 countries. Generalized Space Time Autoregressive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. The GSTAR model produces a space-time model that adopts the stages of the Autoregressive Integrated Moving Average (ARIMA) model. This research uses parameter estimation using the Maximum Likelihood method, which is a method used to estimate parameter values by maximizing the probability function seen based on observations. This research uses secondary data in the form of Stock Price Index data from 5 countries in Asia, namely the Composite Stock Price Index (JCI), Philippine Stock Exchange (PSEi), Strait Time Index (STI), Kuala Lumpur Composite Index (KLCI), and Thailand Stock Exchange Index (SETI). Stock Price Index data was divided into in-sample data for Generalized Space-Time Autoregressive (GSTAR) modelling and out-sample data used to validate presumptive results. In-sample data was taken from January 4, 2021, to December 29, 2023, and then out-sample data for presumptive was as many as 5 from January 2, 2024, to January 8, 2024. From the modeling results, it was found that the mean MAPE value of the GSTAR model was smaller than that of the ARIMA model. Moreover, based on the presumptive results for the following 5 periods using the GSTAR (2.1) I(1) model, a Mean Absolute Percentage Error (MAPE) of less than 10% in each location. The values shows that GSTAR model is more accurate than the ARIMA model.
Co-Authors AA Sudharmawan, AA Anis Rahayu Kabul Trifiyanto Kabul Trifiyanto Ma'rifah, Diana Nur Khasanah Pradani, Augistri Putri Putri Pradani, Augistri Siti Nur Azizah, Siti Nur Tuti Zakiyah Tuti Zakiyah Tuti Zakiyah Tuti Zakiyah Zakiyah, Tuti